Text Classification
Transformers
TensorBoard
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use chamdentimem/final_nlp_question1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use chamdentimem/final_nlp_question1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="chamdentimem/final_nlp_question1")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("chamdentimem/final_nlp_question1") model = AutoModelForSequenceClassification.from_pretrained("chamdentimem/final_nlp_question1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from chamdentimem/final_nlp_question1: direct link, hf CLI and curl.
- Browser
- Download file 4.98 kB
-
https://huggingface.co/chamdentimem/final_nlp_question1/resolve/main/training_args.bin
- Command line
-
hf download hf://chamdentimem/final_nlp_question1/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/chamdentimem/final_nlp_question1/resolve/main/training_args.bin
4.98 kB
- Xet hash:
- e962df0a189e1c4c96f401da82208a91b1456c7b3913cc5fcc3b14c7f3a542b5
- Size of remote file:
- 4.98 kB
- SHA256:
- 27224b9da9eb56d11b8e923de56656219add06620f6d2277046ab1c79676c489
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.